Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Fuzzy rule generation methods for high-level computer vision
Fuzzy Sets and Systems
Fuzzy prediction and filtering in impulsive noise
Fuzzy Sets and Systems - Special issue on fuzzy signal processing
A fuzzy neural network model for revising imperfect fuzzy rules
Fuzzy Sets and Systems
Fuzzy engineering
Constructing a fuzzy controller from data
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
The three semantics of fuzzy sets
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Fuzzy control and conventional control: what is (and can be) the real contribution of fuzzy systems?
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Now comes the time to defuzzify neuro-fuzzy models
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Principle of information diffusion
Fuzzy Sets and Systems
A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules
Fuzzy Sets and Systems
Demonstration of benefit of information distribution for probability estimation
Signal Processing - Special issue on fuzzy logic in signal processing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Digital Time Series Analysis
Towards Efficient Fuzzy Information Processing: Using the Principle of Information Diffusion
Towards Efficient Fuzzy Information Processing: Using the Principle of Information Diffusion
Expert Systems with Applications: An International Journal
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
A feature construction approach for genetic iterative rule learning algorithm
Journal of Computer and System Sciences
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In this paper, we use the information matrix technique to extract fuzzy if-then rules from data including noise. With a normal diffusion function, we change all crisp observations of a given sample into fuzzy sets to make an information matrix. We extract rules according to the centroids of the rows of an information matrix. These rules are integrated into an additive fuzzy system with the same rule weight. Such fuzzy systems can be used as adaptive function approximators. Simulations show that this method is very effective compared with the conventional least-squares method and neural network. The best advantage of the suggested method is that, it may be the simplest way to extract fuzzy if-then rules from data.